Toward a Care Process Metamodel: For business intelligence healthcare monitoring solutions
Bibliographic record
Abstract
Improving care processes in healthcare institutions relies on effectively monitoring and making timely decisions for improving patient experience. Business Intelligence solutions have proven to be effective for monitoring processes in other industries. However, healthcare organizations face three challenges for implementing Business Intelligence solutions that effectively monitor care processes. First, the great variation of processes in healthcare domain makes it difficult to model them. Second, there is a gap between abstract administrative indicators and fine-grained operation-level measures of healthcare processes. Finally, it is difficult to reuse the underlying healthcare processes used for other successful solutions. In this paper, we present a Care Process Metamodel geared towards modeling healthcare processes. This metamodel (a) provides a platform for creating uniform care processes, (b) enables hierarchical care processes for modeling of composite processes as well as bridging the gap between abstract performance indicators and operation-level measures of healthcare processes, and (c) facilitates reusing the processes and the data structures required for monitoring them. This metamodel thus addresses some of the challenges for implementing successful Business Intelligence care process monitoring solutions for healthcare organizations. We also demonstrate how the Care Process Metamodel-based processes fit into an architecture, where data collected about encounters of patients can be used by stakeholders for improving the process and its execution. We use samples of cardiac-related processes to illustrate our approach.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".